Optimal Energy-Driven Aircraft Design Under Uncertainty

Aerodynamic shape design robust optimization is gaining popularity in the aeronautical industry as it provides optimal solutions that do not deteriorate excessively in the presence of uncertainties. Several approaches exist to quantify uncertainty and, the dissertation deals with the use of risk measures, particularly the Value at Risk (VaR) and the Conditional Value at Risk (CVaR). The calculation of these measures relies on the Empirical Cumulative Distribution Function (ECDF) construction. Estimating the ECDF with a Monte Carlo sampling can require many samples, especially if good accuracy is needed on the probability distribution tails. Furthermore, suppose the quantity of interest (QoI) requires a significant computational effort, as in this dissertation, where has to resort to Computational Fluid Dynamics (CFD) methods. In that case, it becomes imperative to introduce techniques that reduce the number of samples needed or speed up the QoI evaluations while maintaining the same accuracy. Therefore, this dissertation focuses on investigating methods for reducing the computational cost required to perform optimization under uncertainty. Here, two cooperating approaches are introduced: speeding up the CFD evaluations and approximating the statistical measures. Specifically, the CFD evaluation is sped up by employing a far-field approach, capable of providing better estimations of aerodynamic forces on coarse grids with respect to a classical near-field approach. The advantages and critical points of the implementation of this method are explored in viscous and inviscid test cases. On the other hand, the approximation of the statistical measure is performed by using the gradient-based method or a surrogate-based approach. Notably, the gradient-based method uses adjoint field solutions to reduce the time required to evaluate them through CFD drastically. Both methods are used to solve the shape optimization of the central section of a Blended Wing Body under uncertainty. Moreover, a multi-fidelity surrogate-based optimization is used for the robust design of a propeller blade. Finally, additional research work documented in this dissertation focuses on utilizing an optimization algorithm that mixes integer and continuous variables for the robust optimization of High Lift Devices

Aerodynamic models for high-amplitude, low reynolds flapping airfoils

In this thesis a new aerodynamic model of insect-like flapping flight for micro air vehicles has been developed. The New Predicted Aerodynamic Model (NPAM) was based on the model described by Weis-Fogh model in Energetics of Hovering Flight in Hummingbirds and Drosophila. In order to achieved the NPAM some variations were introduced regarding the geometry of the problem under study and also some improvements was done to the theory developed by Weis-Fogh. To have the required inputs for the model, a fitting from a DNS database for lift and drag coefficient, and aerodynamic efficiency was performed. Finally, the DNS database was also used to determine the accuracy and validity of the New Predicted Aerodynamic Model.Ingeniería Aeroespacia

Towards a privacy aware information system for emergency response

Intelligent Systems in Smart Cities capture and exchange a large variety of information, for example, environmental data, location, biometric and personal data, health records, among others, in order to improve the quality of services. On one hand, such systems could represent an important, lifesaving resource for public services aimed at addressing emergency situations (e.g. firefighters, police), by providing access to a large amount of diverse information. On the other hand, they are also a threat with respect to data protection and privacy when disclosing all sort of personal and sensitive information. Since not all the information available can be used or helpful for handling the emergency we have the challenge of ensuring that the least possible amount of sensitive information is exchanged, therefore reducing the risk of unwanted disclosure and misuse. Thus, being aware and include a privacy-by design approach when managing personal and sensitive data is essential in the context of emergency systems. This work aims to analyse the privacy issues that Intelligent Systems face when sharing information with public services to attend emergency situations. By characterising these issues, we aim of informing the knowledge requirements for designing an Intelligent System that only allows valuable and helpful information to be exchanged, minimising personal and sensitive data disclosure

Health Condition Evolution for Effective Use of Electronic Records: Knowledge Representation, Acquisition, and Reasoning

Smart City initiatives aim to enhance the effective management of resources while providing quality services to citizens. Central to these initiatives is the use of large-scale datasets that enable intelligent analytics and reasoning components in support of resource optimisation and service provision. Recently, there has been a growing interest in aspects of smart living, particularly due to the increasing adoption and use of Electronic Health Records (EHR). A Smart City can introduce intelligent systems to support the usage of EHR to improve emergency response services. For instance, data derived from EHR is used in primary emergency care, as a component of emergency decision support systems and for monitoring public health. However, the delivery of healthcare information to emergency bodies must be balanced against the concerns related to citizens’ privacy. Besides, emergency services face challenges in interpreting this data; the heterogeneity of sources and the large amount of available information represents a significant barrier. This thesis investigates the use of EHR for deriving useful information about people requiring assistance during an emergency, focusing on making rich data accessible to emergency services while minimising the amount of exchanged information. To perform this task, an intelligent system needs to estimate the probability that a potentially relevant condition mentioned in a health record is still valid at the time of the emergency. During our research work, we followed a knowledge engineering approach and developed the required knowledge components to support the intelligent delivery of relevant health information about people involved in an emergency situation. These components, which include a knowledge component for representation and reasoning, and a novel knowledge base modelling the evolution of a large number of health conditions, form the basis of CONRAD, a system which is able to support effectively decision-making in an emergency scenario

Regulatory Immunotherapy in Bone Marrow Transplantation

Every year individuals receive hematopoietic stem cell transplantation (HSCT) to eradicate malignant and nonmalignant disease. The immunobiology of allotransplantation is an area of ongoing discovery, from the recipient's conditioning treatment prior to the transplant to the donor cell populations responsible for engraftment, graft-versus-host disease, and graft-versus-tumor effect. In this review, we focus on donor-type immunoregulatory T cells, namely, natural killer T cells (NKT) and regulatory T cells (Treg), and their current and potential roles in tolerance induction after allogeneic HSCT

HECON: Health Condition Evolution Ontology

Health records contain extensive information, including conditions, test results, procedures, and appointments. These data reveal past medical observations that could allow drawing a picture of the current health state of a patient. Widely adopted clinical terminology taxonomies, such as SNOMED CT and the FHIR standard, facilitate the processing and exchange of electronic health records. However, despite these efforts, the task of estimating whether a particular condition is affecting a patient's health at a certain point in time is not supported yet.This paper introduces HECON, the Health Condition Evolution Ontology that represents the evolution of health conditions over time. This representation enables reasoning on possible ongoing health issues derivable from patients' health records for the benefit of intelligent systems in the emergency domain. We describe the process for building the ontology and the application of HECON in a fire emergency scenario. We design the ontology following established ontology engineering practices, including Competency Questions and ontology reuse. Furthermore, we construct a Knowledge Graph from a database of extracted Health Evolution Statements and use it to validate the consistency and requirements of the HECON Ontology

CONRAD - Health Condition Radar: an Intelligent System for Emergency Support

Smart City initiatives have emerged as a technological solution to enhance the use of resources and improve city services. Emergency Management and Support is attracting considerable attention in this context, and several smart solutions have been proposed to support emergency services activities. On the one hand, Electronic Health Records (EHR) data allows an emergency response system to derive a person’s current health status and consequently use this information to advise emergency bodies about people with ongoing health issues requiring assistance. On the other hand, using such comprehensive and detailed data has its challenges. EHR contain an overwhelming amount of information that emergency services cannot process effectively, for both its size and specificity. Furthermore, an intelligent system automatically analysing this data will require some knowledge for representing and reasoning over the evolution of health events. This demo paper proposes a software architecture for using EHR to extract information that supports emergency services activities. The architecture uses Semantic Web technologies as tools to derive people’s ongoing health issues, specifically HECON Ontology and the KG for health evolution information. This demo paper also introduces CONRAD, the software prototype which demonstrates the architecture design in action. The prototype uses a dataset of synthetic health records as data input. Its output is a derived list of people in a vulnerable situation, a summary of their ongoing health issues and related needs

Bioethics in Education

In dynamic ambits, systems have to be maintained in a constant process of adaptation. Thus, in the present chapter, we explore the integration of bioethics in all areas of higher education (physics-mathematics, the engineering sciences, social and administrative sciences, the biological-medical sciences, and the humanities), with the objective of establishing, as an essential part, bioethics in all disciplines of knowledge. All undergraduate university degrees converge in the relation among living beings, through knowledge-based interdisciplinary or multidisciplinary study. A close relationship has to be established between education and bioethics within the context of higher education, as teaching at the university level with values and ethics, achieves a contribution to the science of industry in terms of a greater professional ethical sense. Therefore, this work concludes that bioethics should form a fundamental part of every university undergraduate degree